neurocaps.analysis.CAP.caps2corr

CAP.caps2corr(output_dir=None, suffix_title=None, suffix_filename=None, show_figs=True, save_plots=True, return_df=False, save_df=False, **kwargs)

Generate Pearson Correlation Matrix for CAPs.

Produces a correlation matrix of all CAPs and visualizes it using seaborn.heatmap. Can also produce a pandas Dataframe of the correlation matrix where each element contains its uncorrected p-value in parenthesis, with a single asterisk if < 0.05, a double asterisk if < 0.01, and a triple asterisk < 0.001. Note, if groups were given when the CAP class was initialized, separate correlation matrices will be generated for all groups.

Parameters:

• output_dir (os.PathLike or None, default=None) -- Directory to save plots (if save_plots is True) and correlation matrices DataFrames (if save_df is True). The directory will be created if it does not exist. If None, plots and dataFrame will not be saved.

• suffix_title (str or None, default=None) -- Appended to the title of each plot.

• suffix_filename (str or None, default=None) --

  Appended to the filename of each saved plot if output_dir is provided.

  Added in version 0.19.0.

• show_figs (bool, default=True) -- Display figures.

• save_plots (bool, default=True) -- If True, plots are saves as png images. For this to be used, output_dir must be specified.

• return_df (bool, default=False) -- If True, returns a dictionary with a correlation matrix for each group.

• save_df (bool, default=False,) -- If True, saves the correlation matrix contained in the DataFrames as csv files. For this to be used, output_dir must be specified.

• kwargs (dict) --

  Keyword arguments used when modifying figures. Valid keywords include:

  • dpi: int, default=300

    Dots per inch for the figure. Default is 300 if output_dir is provided and dpi is not specified.

  • figsize: tuple, default=(8, 6)

    Size of the figure in inches.

  • fontsize: int, default=14

    Font size for the title each plot.

  • xticklabels_size: int, default=8

    Font size for x-axis tick labels.

  • yticklabels_size: int, default=8

    Font size for y-axis tick labels.

  • shrink: float, default=0.8

    Fraction by which to shrink the colorbar.

  • cbarlabels_size: int, default=8

    Font size for the colorbar labels.

  • xlabel_rotation: int, default=0

    Rotation angle for x-axis labels.

  • ylabel_rotation: int, default=0

    Rotation angle for y-axis labels.

  • annot: bool, default=False

    Add values to each cell.

  • annot_kws: dict, default=None,

    Customize the annotations.

  • fmt: str, default=".2g",

    Modify how the annotated vales are presented.

  • linewidths: float, default=0

    Padding between each cell in the plot.

  • borderwidths: float, default=0

    Width of the border around the plot.

  • linecolor: str, default="black"

    Color of the line that seperates each cell.

  • edgecolors: str or None, default=None

    Color of the edges.

  • alpha: float or None, default=None

    Controls transparency and ranges from 0 (transparent) to 1 (opaque).

  • bbox_inches: str or None, default="tight"

    Alters size of the whitespace in the saved image.

  • cmap: str, callable default="coolwarm"

    Color map for the plot cells. For this parameter, you can use pre-made color palettes or create custom ones. Below is a list of valid options:

    • Strings to call seaborn's pre-made palettes.

    • seaborn.diverging_palette function to generate custom palettes.

    • matplotlib.color.LinearSegmentedColormap to generate custom palettes.

  • vmin: float or None, default=None

    The minimum value to display in colormap.

  • vmax: float or None, default=None

    The maximum value to display in colormap.

Returns:

dict[str, pd.DataFrame] -- An instance of a pandas DataFrame for each group.

Note

Color Palettes: Refer to seaborn's Color Palettes for valid pre-made palettes.